424,651 research outputs found
Assisted Dictionary Learning for fMRI Data Analysis
Extracting information from functional magnetic resonance (fMRI) images has
been a major area of research for more than two decades. The goal of this work
is to present a new method for the analysis of fMRI data sets, that is capable
to incorporate a priori available information, via an efficient optimization
framework. Tests on synthetic data sets demonstrate significant performance
gains over existing methods of this kind.Comment: 5 pages, 2 figure
Finding Motif Sets in Time Series
Time-series motifs are representative subsequences that occur frequently in a time series; a motif set is the set of subsequences deemed to be instances of a given motif. We focus on finding motif sets. Our motivation is to detect motif sets in household electricity-usage profiles, representing repeated patterns of household usage. We propose three algorithms for finding motif sets. Two are greedy algorithms based on pairwise comparison, and the third uses a heuristic measure of set quality to find the motif set directly. We compare these algorithms on simulated datasets and on electricity-usage data. We show that Scan MK, the simplest way of using the best-matching pair to find motif sets, is less accurate on our synthetic data than Set Finder and Cluster MK, although the latter is very sensitive to parameter settings. We qualitatively analyse the outputs for the electricity-usage data and demonstrate that both Scan MK and Set Finder can discover useful motif sets in such data
Analysis And ImplementationOf K-Mean And K-Medoids Algorithm For Large Dataset To Increase Scalability And Efficiency
The experiments are pursued on both synthetic in data sets are real. The synthetic data sets which we used for our experiments were generated using the procedure. We refer to readers to it for more details to the generation of large data sets. We report experimental results on two synthetic more data sets in this data set; the average transaction of size and its average maximal potentially frequent item set its size are set, while the number of process in the large dataset is set. It is a sparse of dataset. The frequent item sets are short and also numerous data sets to cluster. The second synthetic data set we used is. The average transaction size and average maximal potentially frequent item set size of set to 30 and 32 respectively. There exist exponentially numerous frequent item data sets in this data set when the support based on threshold goes down. There are also pretty long frequent item sets as well as a large number of short frequent item sets in it. It process of contains abundant mixtures of short and long frequent data item sets
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